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Comparative study on model-scale sloshing tests

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ORIGINAL ARTICLEComparative study on model-scale sloshing testsSang-Yeob Kim •Kyong-Hwan Kim •Yonghwan KimReceived:26March 2011/Accepted:18September 2011/Published online:1November 2011ÓJASNAOE 2011Abstract This paper considers a comparative study on model-scale sloshing tests.There are two primary scopes of this study:the comparison of sloshing pressure measured in 1/50-scale model tests at Seoul National University (SNU)with (1)the data measured at the other facility for the same model,and (2)the data measured on a smaller scale model.For the comparative study,model tanks are excited with the same irregular motions with Froude scale,and sloshing pressure signals are measured at the same locations.The statistical quantities of 1/50-scale model tests are compared with those of other facility and 1/70-scale model tests.In this study,it is found that peak pressure measured at SNU are slightly lower than those of other facility,and this difference may be due to different sensor types and sensing diameters.Keywords Sloshing experiment ÁScaled model test ÁImpact pressure ÁComparative study1IntroductionThe capacity of 138,000–145,000m 3was the most popular in the LNG carrier (LNGC)market from 1970s to 1990s.From the beginning of the 2000s,the construction of larger LNGC has been dramatically increased,and LNGC with capacity over 180,000m 3appeared in the late 2000s.Although the size of LNGC has been dramatically increased,the number of cargo is still four or five.Suchunbalance can result in the significant increase of sloshing loads on liquefied gas tanks.Despite a lot of previous theoretical and computational efforts to predict sloshing pressure,a model-scale test is still considered the most reliable approach for practical purposes.Because of this reason,in the last decade,very systematic methodologies or concepts for the experimental assessment of sloshing loads have been studied [1,2],and a few large experimental facilities have been built for prac-tical model tests.During the last 2years,an experimental system for sloshing has been also being equipped in Seoul National University (SNU)[3].Currently,there are two motion platforms in SNU:a mid-size platform of 4ton capacity and a small-size platform of 1ton capacity.This study primarily aims at the comparison of two existing facilities.Since existing facilities have different motion platforms and pressure measurement systems,the comparison of experimental results under the same excita-tion conditions is very meaningful and important for checking the consistence of the results.In this study,a 1/50-scale tank model with existing experimental data measured at the other experimental facility is chosen for such com-parative study,and the sloshing experiment is carried at SNU facility for the same tank model.The pressure signals of 5-h time window in real scale are measured and statistical analysis are carried out for peak pressures.Those results are compared with other facility’s results,and their differences are observed.This study is extended to a series of experiment for a 1/70-scale model with the same shape,and experimental results are compared with those of the 1/50-scale model.In this experiment,excitation motions are properly scaled by using the 1/50-scale test cases in order to apply the same motion characteristics,but the same pressure sensors are equipped.That is,the sizes of sensing area are different if those areS.-Y.Kim ÁK.-H.Kim ÁY.Kim (&)Department of Naval Architecture and Ocean Engineering,Seoul National University,588,Gwanak-ro,Gwanak-gu,Seoul 151-744,Koreae-mail:yhwankim@snu.ac.krJ Mar Sci Technol (2012)17:47–58DOI 10.1007/s00773-011-0144-zscaled up to real-ship size.Generally,to get consistency in scaled model tests,the sensing diameter of pressure sensors should be properly scaled for different model scales.How-ever,in the actual situation(s),finding a set of properly-scaled sensors is very hard.Therefore,it is very important to know the effects of the sensor size.In this paper,some key findings from this comparative study are discussed.2Experimental setupThis study has two primary objectives:comparison of sloshing loads measured(1)at two different facilities for the same tank model and excitation condition,and(2)for different scale models under the same excitation motion. To this end,two motion platforms in Seoul National Uni-versity are used.The motion platforms are hexapod type with six actuators,as shown in Fig.1.The capacities of the platforms are4and1ton,and both can generate6-DOF regular and irregular motions.The two models considered in these tests are the1/50and 1/70scaled tanks of a140,000LNGC.The1/50-scale model is tested by using the motion platform of4-ton capacity, while the other is installed on the platform of1-ton capacity. The tank models are made of transparent acrylic plates.The thicknesses of the plates are20mm in the1/70-scale tank and35mm in the1/50-scale tank,respectively.The media filled inside the tanks are air and water.Figure2shows the two different scaled tank models and Fig.3shows the dimensions of1/50-scale model.The dimensions of tank models are decided by Hyundai Heavy Industry(HHI).Sloshing pressure is measured by using211B5sensors which are integrated circuit piezoelectric(ICP)-type sen-sors made by Kistler.Sensor diameter is5.54mmandMid-size (4 ton) b Small-size (1 ton)aFig.16-DOF motionplatforms1/50-scale 1/70-scalebaFig.2Tank modelsmaximum pressure range is 7bars.The 1/50-scale tests reproduce the existing experiments performed at MARINTEK,hereafter named as the other facility in the text.In this case pressure sensors with the sensing diameter of 2mm,manufactured by Kulite Co.,were used.There-fore,there is a strong possibility that the difference of two sensors,particularly sensor diameters,can be the primary source of the discrepancy of the data measured at two facilities.The positions of the pressure sensors are shown in Fig.4.Sensor positions are chosen to be consistent between the two tests.In recent experimental studies for sloshing,sampling the rate of 20kHz is widely applied [4,5].Most recently,Nitin et al.[6]carried out a comparative experiment with varying sampling rates from 20to 100kHz and suggested20–40kHz as a reasonable range of sampling rate.In this study,the sampling rate of pressure measurement is set to 20kHz in both the 1/50and 1/70scale tests.It means,sampling rate is not exactly scaled.For better consistency,the sampling rate should be scaled properly,and such scaling will be considered in future work.Table 1summarizes the test conditions of the present experiment.In this study,five filling levels are considered,and one sea-state per each filling condition is applied.Although the motion platforms can generate 6-DOF motions,4-DOF motions are applied in the present study,since the previous experiment at the other facility has been carried under 4-DOF excitation.The input motion signals for the excitation platform are exactly the same to those applied at the other facility,and pressure signals are95% and 70% filling 50% filling30% filling10% fillinga b c d Fig.4Positions of pressure sensors (darker locations indicate the activesensors)Fig.3Dimensions of 1/50-scale modelmeasured during 5h in real time In addition,Froude scaling is used to scale the motion and pressure up or down.3Analysis methodIn early times,Mathiesen [7]and Gran [8]introduced pioneering researches on a statistical approach to predict extreme pressure values.Mathiesen introduced theapplication of Weibull distribution for a set of sampled peak pressure values.Gran compared the goodness of fit-ting to Weibull and Fretchet distributions,and this study has been the theoretical background of many researches afterward [9–11].In this paper,statistical analysis is carried out to com-pare the test results.The threshold pressure and sampling time-interval for peak samples are set to 5.0kPa and 0.2s in the 1/50-scale tests,and 3.6kPa and 0.17s in theSNUOther facilitya b Fig.5Sloshing impact signals from two facilities:10%filling,channel no.=18Table 1Experimental conditions in real scale:L (tank length),H (tank height)DescriptionShip model HHI 140K LNG carrier Filling level 10%(0.19L a )30%(0.39H )50%(0.59H )70%(0.79H )95%(0.959H )Sea-stateH s =7.01m,T z =6.0sH s =12.82m,T z =8.5sWave heading angle (°)90030Simulation time5ha10%filling:water depth =0.19L ,all others are with respect to Ha SNUb Other facilityFig.6Sloshing impact signals from two facilities:95%filling,channel no.=1395% filling: SNU95% filling: Other facility70% filling: SNUc b aFig.7Pressure signals of three largest sloshing impacts measured at different facilities70% filling: Other facilitye d 30% filling: SNUf 30% filling: Other facilityFig.7continued1/70-scale tests.It was presented that statistical results are not very sensitive to the threshold pressure and sampling time-interval as long as they are not very small or large [12].As a temporal parameter of sloshing-induced peaks,rising time is considered.In this study,rising time is defined as follows:T rise ¼2t P max Àt ð0:5P max Þup -crossing ÀÁ:ð1Þwhere t P max is the time when peak pressure P max occurs,and t ð0:5P max Þup -crossing is up-crossing time when pressure becomes 0.5P max .The average of the (1/n )largest peaks and probable extreme pressure are observed for the comparison of sta-tistical properties.Particularly,three-parameter Weibull and Pareto distribution functions are considered to repre-sent the extreme distribution function of peak pressures.The cumulative distribution function of three-parameter Weibull distribution can be written as F ðx Þ¼1Àexp Àðx Àd Þ=b ½ c ðÞð2Þwhere d is the location parameter,b is a scale parameter,and c is a shape parameter.Here,x should be larger than the location parameter,d .To estimate these three parameters,the method of moments can be applied.In this method,the parameters are estimated by matching thefirst three model moments,mean (l ),variance (r 2),and skewness (c 1)with corresponding sample moments.Themean,variance and skewness are given byl ¼b C 1þ1cþdð3Þr 2¼b 2C 1þ2c ÀC 21þ1c!ð4Þc 1¼C 1þ3c À3C 1þ1c C 1þ2c þ2C 31þ1cC 1þ2c ÀC 21þ1ch i 3=2ð5Þwhere C is the Gamma function.In order to establish a model which gives a better fit to the larger values in the tail,a peak-over-threshold (POT)method can be utilized for fitting the sample data.The POT method uses only the data which exceeds a certain threshold value,and the cumulative distribution function of peaks is described asF ðx Þ¼F X ðx Þ1ÀF ðu Þ½ þF ðu Þð6Þwhere u is the threshold value,and F X (x )is the distribution of pressure peaks over threshold.x should be larger than95% filling 70% filling30% filling 10% fillingc a bd Fig.8Comparison of the scatter diagrams of sloshing peaks measured at different facilities (from all pressure sensors)the threshold value.According to Pickands [13],F X (x )asymptotically follows the generalized Pareto distribution model,and the cumulative distribution function of this model is given byG ðx Þ¼1À1þcx =k ðÞÀ1=c if c ¼01Àexp Àx =k ðÞif c ¼0&ð7Þwhere the range of x is 0\x \?for c C 0and0\x \-c /k for c \0.k is a scale parameter and c is a shape parameter.To estimate the parameters,the method of moments can be applied.The mean and variance are given by Bury [14]as l ¼k 1Àcð8Þr 2¼k 2ð1À2c Þð1Àc Þð9Þ4Experimental resultsThe representative sloshing pressure signals measured at two different facilities are presented in Figs.5and 6.Because of the nonlinearity of sloshing phenomena,the same sloshing peaks cannot be expected at the same time between the two cases.However,the patterns of impact signal can be compared.Figure 5shows the pressure sig-nals measured on a port-side wall near a lower chamfer in the 10%filling condition.In the results of SNU,pressure values after the impact is slightly higher than zero,while the pressure becomes lower than zero in the results of other facility.This tendency is shown in most pressure signals of the two tests.Such difference seems to be due to the dif-ference of sensor types.Figure 6shows the examples of impact signals measured at the tank top in the 95%filling level,and the oscillations seem to be caused by air bubbles or large air pockets.In this condition,the pressure patterns of the two tests look similar.Figure 7shows the pressure signals of three largest sloshing peaks among the all channels in three filling lev-els.Locations where the largest sloshing peak measured show good agreement between two tests,and the signals look similar in all the filling cases although the magnitudes of peak pressures shows some differences.This comparison proves that pressure measurements at the two test facilities are comparable.The temporal evolution and peak value of the impacts are the two main issues of sloshing-induced loads.Figure 8shows the scatter diagrams of sloshing peaks in different filling levels.x and y axes represent rising time and peak pressure,respectively.These scatter diagrams show fair agreement.In terms of rising time the best agreement seems to occur for 70%filling while in terms of maximum pressure peak it seems to occur for 30%filling.The most scattered tendency can be observed in the 30%filling condition,while the most concentrated distribution shows in the 70%filling condition.It should be noticed that,in the 70%filling,the rising times of all peaks are less than 1.5ms.Figure 9shows the bar-type plots of pressure peaks measured at the two different facilities.The values in these plots are from the pressure signals of all sensors.It can be found that the magnitudes of pressure peaks look similar except for one or two extraordinary high peaks.There is one large peak in the 50%filling case,which was measured at the other facility.This peak can give a significant effect to the statistical results and disturb an appropriate com-parison of the two tests.In addition,it should be mentioned that the numbers of sloshing peaks measured in the 50%filling level are relatively small when compared to those of other filling level tests,indicating that sloshing flow is not violent as much as other filling conditions.95% filling50% filling30% fillinga cb Fig.9Comparison of time-histories of sloshing peak measured at different facilities (from all pressure sensors)The exceedance probability diagrams for the measured sloshing peaks of the two tests are shown in Fig.10.In all filling levels,the Weibull distributions of other facility’sresults are more weighted toward the right than those of SNU.It means that the most probable extreme pressures of the other facility are larger than those of SNU considering the same cumulative probability.The statistical results of the present 1/50-scale tests are summarized in Fig.11which shows the ratios of the peak pressures measured at SNU with respect to those measured at the other facility.As shown in this comparison,the ratios are smaller than 1.0in all filling levels and the overall ratios are close to 0.8.This means that the sloshing peaks measured at SNU are relatively smaller than those mea-sured at the other facility.In the case of the 95%filling condition,the ratio is close to 1.0,while in the case of the 70%filling condition,the ratio of 3-h extreme values is close to 0.6.When consid-ering the different sensor diameters of pressure sensors,this trend can be acceptable.In most cases except the 30%condition,the ratios for the 3-h extreme pressures are smaller than those of averaged 1/10largest values.It means that,as a higher cumulative probability is considered,the gap between the corresponding extreme pressures of the tests at two facilities becomes bigger.The number of sloshing peaks measured in the different tests is reported in Table rge numbers of peak samples do not guarantee the good agreement between the results of the two facilities.However,if the number of peak samplesTable 2Numbers of measured sloshing peaks from the tests con-ducted by two different facilities (accumulated from all pressure sensors)Filling level 10%30%50%70%95%SNU 19773993159654Other facility16075612734483995% filling 70% filling30% filling 10% fillinga b c d Fig.10Exceedanceprobabilities for sloshing peaks (from all pressuresensors)Fig.11Statistical results:ratio of peak pressure measured at SNU’s result to that of other facility,data from all sensorsis small,statistical results get less confidence and have more uncertainty.By cross-examining results in Table 2with those in Figs.11and 12,the tests with less agreement have relatively smaller pressure impact and less number of sloshing peaks.The ratios of the sloshing pressure at each tank face are compared in Fig.12.Naturally,the statistical results of the pressure peaks are dependent on the tank face.For exam-ple,in the 95%filling condition,the ratio at the port upperchamfer is very close to 1.0,while the ratio at the forward bulkhead is lower than 0.5.A positive fact observed from the present comparison is that the results between the two facilities show good correspondence in the regions where high impact pressures occur, e.g.the port-side upper chamfer in 95,50and 30%fillings,tank top in 70%filling,and port-side wall in 10%filling.As mentioned,it is very possible that the main source of the difference between the results of the two facilities is the95% filling 70% filling30% filling10% fillinga b c d Fig.13Statistical properties of tests in real scale (from all pressure sensors)95% filling 70% filling30% filling10% fillingb acd Fig.12Statistical results:ratio of peak pressure measured at SNU’s result to that of other facility,data from the sensors at each tank facedifferent diameters of two sensors.To examine further the effects of the sensor diameter,a series of1/70-scale model test are carried out with the sensors which are used for the 1/50-scale model tests.Pressure sensors should be properly scaled up or down as the model scale changes.However,it is not easy to change the size of sensor in a real situation. Furthermore,based on the experimental experience and/or due to some reasons,each facility has a preference on dynamic pressure sensors.So,like the present case,two different facilities use difference sensors.Figure13compares the real-scaled statistical properties of three cases,i.e.the impact pressures conducted at dif-ferent test facilities with different model scales.In this study,Froude scaling without density correction is used to translate the pressures of the model scale to those of the real scale.The application of Froude scaling can be an issue in this comparison,but this discussion will not be included in this paper.From the comparison of the statistical results of the 1/50-scale and1/70-scale model tests at SNU,it can be found that the statistical results of the1/70-scale tests show a fair agreement to those of the1/50-scale test in allfilling levels.It means that the effect of the pressure-sensor size with a different scale model test may be highly significant. However,for more solid conclusion,a systematic study with proper scaling for the sensor size,sampling frequency, and test material(e.g.liquid and gas)is essential.5ConclusionsBased on the present comparative studies,the following conclusions are introduced:•In the1/50-scale tests,sloshing loads measured at SNU are smaller than those of measured at the other facility.Such difference seems to be caused mainly by the difference of the sensing diameter and type of pressure sensors.•The difference of the sloshing pressure at70%is higher than any otherfilling conditions.At thisfilling,pressure peaks show narrower rising time than at the other cases, which means that impact pressures are spiky and consequently more localized.Then,the magnitude of the peak pressure can be sensitive to the sensor diameter.•The difference between local sloshing impact pressures measured at SNU and other facility is slightly depen-dent on the measuring positions.It is hard to conclude the reason of such difference,but overall trends are acceptable.•The statistical results of the1/50-and1/70-scale tests show acceptable agreement when Froude scaling isapplied.However,the results of the1/70-scale tests are still smaller than those of the1/50-scale measured at the other facility.•The sloshing impact pressure is sensitive to the type of the pressure sensor.In order to get more reliable conclusions,various types of pressure sensors should be tested and repetition tests should be carried out to reduce the uncertainty of sloshing experiments.Fur-thermore,a large scale test is recommended to inves-tigate the scale effect.Acknowledgments This study was mainly sponsored by Daewoo Shipbuilding and Marine Engineering Co.,Samsung Heavy Industry, STX Offshore&Ship building Co.,Hanjin Transportation Manage-ment System,Hyundai Heavy Industry,and Ministry of Knowledge Economy(MKE)in Korea.A part of the research has been supported also by The LRET-Funded Research Center at SNU for FSI (LRETC).Their supports are greatly appreciated.Authors also thank the administrative support of Engineering Research Institute and RIMSE of Seoul National University.References1.Graczyk M,Moan T,Rognebakke O(2006)Probabilistic analysisof characteristic pressure for LNG tanks.J Offshore Mech Arct Eng128:133–1442.Kuo JF,Campbell RB,Ding Z,Hoie SM,Rinehart AJ,Sands-tro¨m,RE,Yung TW,Greer MN,Danaczko MA(2009)LNG tank sloshing assessment methodology—the new generation.In:Pro-ceedings of the19th international offshore and polar engineering conference,ISOPE,Osaka,Japan3.Advanced Marine Technology(2010)Development of large scalesloshing experiment facilities.AMEC Project Report,Seoul National University,Seoul,Korea4.Kim HI,Kwon SH,Park JS,Lee KH,Jeon SS,Jung JH,Ryu MC,Hwang YS(2009)An experimental investigation of hydrody-namic impact on2-D LNGC models.In:Proceedings of the19th international offshore and polar engineering conference,ISOPE, Osaka,Japan5.Maillard S,Brosset L(2009)Influence of density ratio betweenliquid and gas on sloshing model test results.In:Proceedings of the19th international offshore and polar engineering conference, ISOPE,Osaka,Japan6.Nitin R,Tam T,Krish T,Dominique R,Robert KM,Timothy F(2010)The effect of sampling rate on the statistics of impact pressure.In:Proceedings of the29th international conference on ocean,offshore and arctic engineering,OMAE,Shanghai,China 7.Mathiesen J(1976)Sloshing loads due to random pitching.NorMarit Res4(3):2–138.Gran S(1981)Statistical distributions of local impact pressures inliquid sloshing.Nor Marit Res9(2):2–129.American Bureau of Shipping(2006)Guidance notes on strengthassessment of membrane-type LNG carriers.Guidance note (updated in2009),Houston,USA10.Det Norske Veritas(2006)Sloshing analysis of LNG membranetanks.Classification notes,Oslo,Norway11.Ryu MC,Jung JH,Jeon SS,Hwang YS,Han SK,Kim YS,ChoTI,Kwon SH(2009)Reference load for a conventional138K CBM LNG carrier in a comparative approach.In:Proceedings of the28th international conference on ocean,offshore and arctic engineering,OMAE,Honolulu,Hawaii,USA12312.Kim Y,Kim SY,Yoo WJ(2010)Statistical evaluation of localimpact pressures in sloshing.In:Proceedings of the20th inter-national offshore and polar engineering conference,ISOPE, Beijing,China 13.Pickands J III(1975)Statistical inference using extreme orderstatistics.Ann Stat3(1):119–13114.Bury KV(1975)Statistical models in applied science.Wiley,New York123。

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Research on Hierarchical Interactive Teaching Model Based on Naive Bayesian ClassificationDongyan FanInformation faculty, Business College of Shanxi University, Taiyuan 030031, ChinaAbstract—The purpose of this research is improving the current inject classroom teaching mode that ignores individual differences and inefficiency of students. By studying classification algorithm in data mining and applying the classification method based on Naive Bayes algorithm, we designed and implemented scientific classification of students, and draw lessons from stratified and interactive teaching mode, so as to builded a new effective teaching mode. The results show that through scientific classification of students, real-time hierarchical interaction teaching effectively stimulate students' interest in learning, improve cooperation ability, and improve classroom teaching efficiency.Keywords—Naive Bayesian; student classification; hierarchical interactive; teaching modelI.I NTRODUCTIONUnder the background of big data era, the current teaching mode is not adapt to the cultivation of innovative talents, there are many problems, such as low efficiency of classroom, teachers' manipulation of teaching process, ignore the individual differences of students in knowledge transfer ability. Therefore, this study aimed at these problems, by studying classification algorithm in data mining and applying the classification method based on Naive Bayes algorithm, we design and implement scientific classification of students, and draw lessons from stratified and interactive teaching mode, so as to build a new effective teaching mode. The mode enable students to learn efficiently, so as to adapt to the trend of rapid development of new technology and cultivate innovative talents.II.R ESEARCH M ETHODThe research and practice of the hierarchical interactive teaching model based on the Naive Bayesian classification is based on the classification of students' differences. So there are two major tasks need to do: the approaches to the students' difference measurement and grouping and the design of hierarchical interactive teaching framework. Its method flow is shown in Figure I.FIGURE I. RESEARCH METHOD FLOWFirst of all, based on the samples, the naive Bayes algorithm according to the student's attribute value is used to test the students' differences. Then, according to the results to make a scientific difference classification to achieve effective grouping for students. At the same time, the design of the hierarchical interactive teaching framework is carried out by the two subjects (the student is the main body, the teacher is the leading part). Finally, the teaching effect is evaluated and analyzed.III.S TUDENT C LASSIFICATION D ESIGN B ASED ON N AIVEB AYESIANA.Naive Bayesian Theoretical PrincipleAt present, there are many kinds of algorithms in data mining, such as based on Bayes algorithm, decision tree algorithm, neural network algorithm, rough set algorithm, genetic algorithm, support vector machine algorithm and so on. In the practical application of many classification algorithms, the most widely used algorithm is Naive Bayesian algorithm model. Naive Bayes is a simple and effective classification model.From Bayes’ theorem recall that:()()()||P A B P BP B AP A= (1)Equation (1): P(A) and P(B) separate representation the probability of occurrenceof events A andevents B.()|P A B indicates the probability of occurrence of event A under the premise that event B occurs. ()|P A B is a priori probability, and its value is often easily obtained.()|P B A indicates the probability of occurrence of event B under the premise that event A occurs. ()|P B A is a posteriori probability, and its value is the result of the solution of the Bayesian formula.The classifier structure diagram based on the naive Bayes algorithm is shown in Figure II. It’s leaf node Am represents the m attribute, and the root node C represents the category. Suppose {},,D C A S=are training samples, it includes the studentcategory {}12,,iC C C C= and the student attribute {}12,,mA A A A= .Suppose {}12,,nS S S S= represents acollection of classified students, in whichnS represents nthstudent. Suppose {}12,,k mX a a a= is a student to be classified,International Conference on Computer Science, Electronics and Communication Engineering (CSECE 2018)in which each m a represents an attribute eigenvalue of the pending item k X .FIGURE II. THE CLASSIFIER STRUCTURE DIAGRAMB. Design the Individualized Attributes of StudentsThe student classification method based on the naive Bayes algorithm is used the information of the past students as the sample set , which is used to construct the naive Bayes classifier.Students are classified according to the information of the students' attributes. The students divided into the same category are not simply using the score as criterion of evaluation. Its are classified by comprehensive evaluation after combination of other attributes.The difference classification based on the naive Bayes algorithm is select the individual attributes of the students as shown in Figure III. The students which 8 attribute values similar in the two dimensions (character and learning style) are put into one category, while the 12 attributes values of the three dimensions of personal basic situation, learning interest and cognitive ability are different. The purpose of the classification is to carry out differential teaching to implicit dynamic stratification and heterogeneous cooperation for students'cognitive ability, learning interest and basic information.FIGURE III. INDIVIDUALIZED ATTRIBUTES OF STUDENTSC. Student Classification Design Based on Naive Bayesian The process based on the naive Bayes classification is shown in Figure IV.FIGURE IV. STUDENT CLASSIFICATION CYCLE FLOW CHARTBASED ON NAIVE BAYES ALGORITHM1)()i P C is set to indicate the frequency of the occurrence of the student category i C in the training sample concentration, that is the category probability. For sample data sets, there are different levels of students in each category, which avoids the discrimination of students.()()i i P C Count C n=　(2)The function ()i Count C represents the number of students belonging to category i which is in the entire student sample collection of S .n represents the total number of the entire student sample collection of S .2)()|j j i P A C a = is set to represent the conditional probability of each characteristic attribute value of the student in the category.()()()|i C j j j j i i P A C Count A a a Count C ===(3)j j A a =indicates that the value of the j attribute is j a .Thefunction ()i C j j Count A a =represents the number of students which the attribute name is j A and attribute value is j a in the i student category.3) ()|k i P X C is set to represent the conditional probability of the students k X to be classified in the student category i C , m represents the number of attributes that describe student differences.()()1||mk i j j i j P X C P A C a ===∏ (4)4) ()j j P A a = is set to represent the probability of the student's attribute j A when the value is j a . ()()j j j j P A Count A a a n===　(5)The function ()j j Count A a = indicates the number when the value of attribute j is j a .5) ()k P X is set to indicate the probability that the student k X should be classified in the training sample concentration. ()()1mk j j j P X P A a ===∏ (6)6) ()|i k P C X is set to represent the conditional probability that the student k X should be classified to category i . ()()()()||k i i i k k P X C P C P C X P X =(7)7) ()max |k P C X is set to represent the maximum category probability of the student k X which should be classified to the student category .()()()(){}max 12|max |,|,,|k k k i k P C X P C X P C X P C X = (8) max C indicates the maximum category of conditionalprobability which is obtained by (8).Finally, (8) is used to calculate the maximum category probability of the students to be classified in the students category. That is the category of the students to be classified. At this point, one classification ends.IV. T HE D ESIGN OF THE H IERARCHICAL I NTERACTIVET EACHING F RAMEWORK The hierarchical interactive teaching model is an independent, inquiring and cooperative teaching model based on the classification of the naive Bayes algorithm. This model breaks the original classroom structure, and takes the interaction of teachers and students as the carrier, and also group autonomy, and let the students as the subject of the class. This model is guided by the task of the problem, and it is based on the students' self-study, and it aims at the completion of the task of the group. This model creates an ecological chain class based on group mutual learning to solve problems. It pays attention to the state of learning and the quality of life for every student. The design of the hierarchical interactive teaching model framework is shown in Figure V.FIGURE V. THE HIERARCHICAL INTERACTIVE TEACHING MODELFRAMEWORKThe four layers of the hierarchical interactive teaching model are closely related to each other, and support each other dynamically with the spiral. The five segments drive each other to form a whole, interlace and connect with each other. This teaching mode makes the classroom an active area for teachers and students to resonate with their ideology and to show their personality together.V.A NALYSIS OF T EACHING E FFECTIn this paper, the teaching effect is analyzed from two aspects by using the method of questionnaire and comparative experiment. First, the experimental class's comparative analysis before and after the experiment is carried out. Then, a comparative analysis between the experimental class and the contrast class is carried out.The comparative data of the experimental class before and after the experiment are shown in Figure VI. From Figure VI, it can be seen that 85.72% of the students have An attitude of approval towards the application of the hierarchical interactive teaching model based on the naive Bayes algorithm in the teaching. There are 70.13% of the students satisfied with the improved teaching effect. At the same time, it can be seen that the students' interest in learning and the ability to communicate and cooperate have improved obviously.FIGURE VI. THE COMPARATIVE DATA OF THE EXPERIMENTALCLASS BEFORE AND AFTER THE EXPERIMENT The comparison between the experimental class and the contrast class is shown in Figure VII. From Figure VII, we can see that students' satisfaction degree, teaching effect satisfaction and group learning atmosphere based on Naive Bayes algorithm classification are higher than those of the contrast class. At the same time, it can be seen that the students' interest in learning and the ability to communicate and cooperate have also been improved.FIGURE VII. THE COMPARISON BETWEEN THE EXPERIMENTALCLASS AND THE CONTRAST CLASSVI.C ONCLUSIONThe comprehensive analysis shows that, in the implementation of the hierarchical interactive teaching model based on the naive Bayes algorithm, the new teaching mode was accepted by the students , it was welcomed by the students. The new teaching mode can improve the ability of learning interest and collaboration of students. It has a very good teaching effect. Experiments show that the classification algorithm based on Naive Bayes has better feasibility and effectiveness in solving student classification problem.However, due to the limited personal time and ability, there are still some shortcomings in the study. In order to better achieve hierarchical interaction teaching mode based on Naive Bayes algorithm and improve teaching effect, we still need to further improve the limitation of applying naive Bayes algorithm, that is, suppose the attributes of students are independent.A CKNOWLEDGMENTThis work was supported by “Research and construction of the practice teaching system of information specialty(J2016138, The major project of teaching reform research in Shanxi Education Department)” and “The optimization and the platform construction of the practice teaching system of information specialty (SYJ201509, The major project of the research on teaching reform Business College of Shanxi University)”. Our special thanks are due to Prof. Ma Shangcai, for his helpful discussion with preparing the manuscript.R EFERENCES[1]Jonathan Rauh. Problems in Identifying Public and Private Organizations:A Demonstration Using a Simple Naive Bayesian Classification[J]. 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A comparative studyof naïve Bayes classifier and Bayes net classifier for fault diagnosis of roller bearing using sound signal[J].International Journal of Decision Support Systems. 2015 Jan 1; 1(1):115-29.[8]Hamse Y Mussa, John BO Mitchell,Robert C Glen.Full “Laplacianised”posterior naïve Bayesian algorithm[J]. Journal of Cheminformatics. 2013 5:37.[9]K. Magesh Kumar, P. Valarmathie. Domain and Intelligence BasedMultimedia Question Answering System[J]. International Journal of Evaluation and Research in Education, Vol. 5, No. 3, September 2016 : 227 – 234.[10][11]Zhijun Wang1, Li Chen, Terry Anderson. A Framework forInteraction and Cognitive Engagement in Connectivist Learning Contexts[J]. International Review of Research in Open and DistanceLearning, Vol. 15,No.2, Apr 2014:121-141.。

英汉人文地理词汇

英汉人文地理词汇

A阿朗索模型Alonso model阿罗定理Arrow’s theoremB保护conservation备选格网分析repertory grid analysis背景理论contextual theory背景效应contextual effect本体论ontology比较成本分析comparative cost analysis 比较优势comparative advantage边疆frontier边疆理论frontier thesis边界boundary边缘带fringe belt辩证法dialectic变动成分components of change变量转换transformation of variables 波斯坦论题Postan thesis博塞洛普论点Boserup thesis博弈论game theory伯克利学派Berkeley School不公平分配选区malapportionment不公正的选区划分gerrymandering不均衡发展uneven development不确定性uncertainty布赖纳争议Brenner debateC财政危机fiscal crisis财政转移fiscal migration参与观察participant observation残差residual测度measurement测量measurement ; survey产业组织industrial organization场所place ；locale超空间hyperspace超前——滞后模型lead-lag models成本结构cost structrue成本面cost surface成本曲线cost curve成本收益分析cost-benefit analysis城市urban ; city城市企业家化urban entrepreneurialism城市村庄urban village城市的职能分类functional classification of cities 城市地理学urban geography城市更新urban renewal城市管理者与守护者urban managers and gatekeepers 城市规模分布city-size distribution城市化urbanization城市集居区barrio城市景观townscape城市起源urban origins城市群conurbation城市社会运动urban social movement城市生活方式urbanism城市生态学urban ecology城市首位律primate city , law of the城市特殊人口聚居区ghetto城市体系urban system城市与区域规划urban and regional planning城乡边缘带rural- urban fringe城乡过渡带rural- urban fringe城乡连续谱rural- urban continum城镇town乘数multipliers承载力carrying capacity尺度scale冲突conflict重组restructuring抽象化abstraction抽样sampling初始工业化protoindustrialization出口加工区export platform出行travel传播diffusion创新innovation粗放农业extensive agriculture村庄village存在主义existentialismD达尔文主义Darwinism大都市带megalopolis大都市劳动力区metropolitan labour area (MLA)大理论Grand Theory大陆架continental shelf带状发展ribbon development代表权representation等费线isodapane等级规模法则rank-size rule等值线isarithms ; isolines ; isopleths抵抗态度NIMBY帝国主义imperialism蒂伯特模型Tiebout model蒂森多边形Thiessen polygon第二住宅second home第三世界Third Word地带zone地点locale地方place地方感sence of place地方效用place utility地方政府local state地方主义regionalism地理信息系统geographical information systems (GIS)地理学geography地理学和分析马克思主义analytical Maxism , geography地理学会geographical societies地理学史geography , history of地理学与公正justice , geography and地理学与伦理ethics , geography and地理学想象力geographical imagination地理战略区域geostrategic regions地理政治变迁geopolitical transition地理政治学geopolitics地理知识论geosophy地名place-names地盘政治turf-politics地图影像与地图map image and map地图学cartography地图学史cartography , history of地形图topographic map地形转换transformation地域territory地域单元问题areal unit problem地域分异areal differentiation地域社会指标territorial social indicator地域性（体）locality地缘政治学geopolitik地租rent地租缺口rent gap调查surveying ; survey调查分析survey analysis定量方法quantitative methods定期集市体系periodic market systems定性方法qualitative methods东方主义orientalism都市区metropolitan area读图map reading杜能模型von ThÜnen model对数--线性模型log—linear modelling多层次模型multilevel modelling多国公司multinational corporation(MNC)多核心模型multiple nuclei model多米诺理论domino theory多维标度multidimensional scaling(MDS)多元社会plural society多元文化主义multiculturalism多元论pluralismE二项分布binomial distribution二元经济dual economyF发达development发展development发展方式mode of development发展极growth pole发展论developmentalism法兰克福学派Frankfurt School法律地理学law , geography of法则law反工业化deindustrialization反馈feadback反事实解释法couterfacture explanation反推法retrogressive approach反证法retroduction范式paradigm范围经济economies of scope犯罪地理学crime , geography of方差分析analysis of variance (ANOVA)方法论个人主义methodological individualism方言dialect方域地理学chorography方志学chorology仿真simulation非参数统计学non-parametric statistics非法占用squatting非法占用者居住区squatter settlement非汇总交通需求模型disaggegate travel demand modelling非均衡发展uneven development非正式部门informal sector菲利普斯曲线Phillips curve分岔bifurcation分成制sharecropping分割cleavage分割的劳动市场segmented labour market分级统计图choropleth map分类区间class interval分类与规划classification and regionalization 分配方式mode of distribution分配者gatekeepers分区模型zonal model分区制zoning分权devolution封建制度feudalism封建主义feudalism风险risk福利地理学welfare geography福利国家welfare state福特制Fordism福特主义Fordism福祉well-being符号学semiology (semiotics)符号互动论symbolic interactionism符号化symbolization服务阶级service class服务业地理学service , geography of辅助数据分析secondary data analysis抚养比dependency ratio腹地hinterland负担系数dependency ratioG概率地图probability map感应perception隔离segregation隔离指数indices of segregation耕作cultivation耕作类型farming , type of耕作业farming更替率replacement rates更新renewal工具主义instrumentalism工业地理学industrial geography工业革命industrial revolution工业惯性industrial inertia工业化industrialization工业区位论industrial location theory工业区位政策industrial location policy功能主义functionalism供给曲线supply curve公共财政地理学public finance , geography of公共地的悲剧tragedy of the commons公共服务业地理学public services , geography of公共管理地理学public administration , geography of 公共物品public goods公共选择理论public choice theory公共政策地理学public policy , geography of公民权citizenship公正justice共产主义communism共同市场common market共线性collinearity共享资源common pool resources关联性relevance关税tariff观念类型ideal types管制学派regulation school光谱分析spectral analysis规范理论normative theory规划planning规模scale规模经济economics of scale国家state国家二元论dual theory of the state国家公园national parks国家机器state apparatus国民生产总值gross national product (GNP)国内生产总值gross domestic product (GDP)过程process过度城市化overurbanization过滤filteringH海洋法law of sea旱作农业dry farming核心—边缘模式core-periphery model核心区域core area合成理论compositional theory合作社cooperative合作主义corporatism黑色经济black economy红线歧视redlining宏观地理学macrogeography后福特主义post-Fordism后工业城市post-industrial city后工业社会post-industrial society后结构主义poststucturalism后马克思主义post-Maxism后现代主义postmodernism后殖民主义postcolonialism厚描thick description互补性complementarity花园城市garden city划分fragmentation划区算法districting algorithm话语discourse环境environment环境感知environmental perception环境决定论environmental determinism ; environmentalism 环境论environmentalism环境审核environmental audit环境影响评价environmental impact assessment环境运动environmental movement环境灾害environmental hazard环境主义environmentalism荒漠化desertification荒野wilderness回归regression会展地理学spectacle , geography of汇总交通模型aggregate travel model混沌chaos混沌概念chaotic conception混合经济mixed economy混合农业mixed farming活动分配模型activity allocation model活动空间activity space霍特林模型Hotelling model或然论probabilism货币地理学money , geography ofJ激进地理学radical geography积极的歧视positive discrimination积累accumulation积累制度regime of accumulation基本供给品merit good基布兹（以色列集体农庄）kibbutz基础设施infrastructure机会成本opportunity cost饥荒famines集合城市conurbation集聚体agglomeration集体collective集体消费collective consumption集约农业intensive agriculture集中化centralization集中化和中心化concentration and centralization 即时生产just-in-time计量革命quantitative revolution计算机辅助制图computer-assisted cartography寄居工人gastarbeitev家庭重构family reconstitution家庭类型family types假说hypothesis价格政策pricing policies价值观values兼职农业part-time farming监测surveillance监督surveillance阶层class阶级class健康与保健地理学health and health care , geography of 交换reciprocity交通travel交通地理学transport geography交易分析transactional analysis郊区suburb校准calibration教育education教育地理学education , geography of街区级变blockbusting结构功能主义structural functionalism结构化理论structraction theory结构马克思主义structural Maxism结构主义structuralism结婚率nuptiality解除管制deregulation解构主义deconstruction解释学hermeneutics进化论Darwinism经济地理学economic geography经济基础infrastructure经济基础理论economic base theory经济人economic man经济一体化形式form of economic integration 经验主义empiricism景观landscape ; landschaft竞争方式genre de vie竞租曲线bid-rent curve敬地情结geopiety救济区zone of depedence聚落settlement聚落连续性settlement continuity距离摩擦friction of distance距离衰减distance decay决策decision-making决定论determinism均衡equilibriumK卡方检验chi square开发development开拓地frontier康德拉季耶夫周期Kondratieff cycles康德主义Kantianism康乐recreation科学园science park可变成本分析variable cost analysis可变收益分析variable revenue analysis可持续发展sustainable development可能论possibilism可修正地域单元问题modifiable areal unit problem 可转移性transferability克里斯塔勒模型Christaller model客籍工人gastarbeitev空间space空间边际spatial margin空间不均衡inequality , spatial空间崇拜spatial fetishism空间费用曲线space cost curve空间分离论spatial separatism空间分析spatial analysis空间结构spatial structure空间经济学space-economy空间科学spatial science空间垄断spatial monopoly空间偏好spatial preference空间的生产production of space空间收益曲线space revenue curve空间相互作用spatial interaction空间性spatiality空间自相关spatial autocorrelation跨国公司transnational corporation扩散diffusionL拉马克主义Lamarck(ian)ism劳动labour劳动分工division on labour劳动过程labour process劳动价值论labour theory of value劳动力市场labour market劳里模型Lowry model勒普拉社会Le Play Society类型数据分析categorical data analysis离散选择模型discrete choice modelling离心力和向心力centrifugal and centripetal forces 理论theory理性选择理论rational choice theory利润面profit surface例外主义exceptionalism历史地理学historical geography历史唯物主义historic materialism联邦制federalism联合主义consociationalism联盟alliance联系linkages连锁linkages连通度connectivity连续占据sequent occupance链式迁移chain migration恋地情结topophilia邻里neighbourhood邻里单元neighbourhood unit邻里效应neighbourhood effect零售业地理学retailing , geography of领地territory领海territorial sea领土territory领土性territoryiality“陆军中尉”研究subaltern studies旅游地理学tourism , geography of绿带green belt绿色革命green revolution逻辑斯蒂模型logit逻辑实证主义logical positivismM马尔可夫过程（或马尔可夫链）Markov processes (or Markov chains )马尔萨斯模型Malthusuan model马克思主义地理学Maxist geography马克思主义经济学Maxist economics满意化行为satisfying behaviour蔓延sprawl贸易trade贸易比价terms of trade门户城市gateway city密度梯度density gradient面surface面谈interviewing苗床地区seed bed location民族nation民族方法学ethnomethodology民族国家nation-state民族统一主义irredentism民族性ethnicity民族志ethnography民族主义nationalism模拟simulation模式model模型modelN南--北North-South男性中心主义phallocentrism难民refugees内城inner city内飞地exclave内涵式研究intensive research内在关系internal relations能量energy能源energy逆城市化counterurbanization 逆向法retroduction逆中心化decentralization年鉴学派Annales School年龄与性别结构age and sex structure 农场划分farm fragmentation 农民peasant农田系统field system农业agriculture农业地理学agriculture geography 农业革命agriculture revolution 农业退化agriculture involution 农业综合企业agribusiness奴隶制度slavery女权主义地理学家feminist geographies P帕累托最优Pareto optimality配置allocation批判理性主义critical rationalism皮雷纳命题Pirenne thesis毗连区contiguous zone偏离—份额模型shift-share model贫困poverty贫困的循环cycle of poverty贫民窟slum贫民区slum平等equality平衡邻里balanced neighourhood 平均信息场mean information field 频率分布frequency distribution频数分布frequency distribution剖面cross-sectionQ歧视discrimination企业区enterprise zone迁徙耕种shifting cultivation迁移migration前工业城市preindustrial city欠发达underdevelopment欠消费underconsumption侵入和演替invasion and succession 囚徒困境prisoner’s dilemma区段section区划regionalization区际人口统计population accounts区位布局模型location-allocation model 区位分析location analysis区位理论location theory区位三角形locational triangle区位商location quotient区位相互依赖locational interdependence区域region区域地理学regional geography ; chorology ; chorography 区域公正territorial justice区域阶级联盟regional science区域经济周期regional cycles区域科学regional science区域联盟regional alliance区域趋同regional convergence ; convergence , regional 区域政策regional policy曲面surface趋势面分析trend surface analysis圈地enclosure权力power全球变暖(与温室效应) global warming (and greenhouse effect )全球未来global futures群落communityR人本主义地理学humanistic geography人口变动模型commodity人口地理学commercial geography人口过剩overpopulation人口金字塔population pyramid人口零增长zero population growth (ZPG )人口密度population density人口普查census人口普查区census tract人口潜力population potential人口预测population projection人类地理学anthropogeography人类能动性human agency人类生态学human ecology人类生态学方法错误ecological fallacy人类主观性subjectivity , human , human subjectivity 人类作用human agency人为灾害hazard , human-made人文地理学human geography人种学ethnography认识论epistemology日常城市体系daily urban system瑞利法则Reilly’s lawS扇形模型sectoral model商品commodity商业地理学commercial geography熵entropy熵最大化模型entropy-maximizing models上层建筑superstructure舍贝里模型Sjoberg model社会society社会达尔文主义social Darwinism社会地理学social geography社会反常状态anomie社会福祉social well-being社会公正social justice社会距离social distance社会空间social space社会理论social theory社会区分析social area analysis社会网络social network社会物理学social physics社会形态social formation社会运动social movement社会再生产social reproduction社会指标social indicator社会主义socialism社区community社团societies绅士化gentrification神圣空间与世俗空间sacred and profane space生产production生产地域综合体territorial production complex (TPC)生产方式mode of production生产力forces of production ; productive forces 生产率productivity生产要素factors of production生产者服务业production services生产综合体production complex生存空间lebensraum生活世界lifeworld生活质量quality of life生命表life table生命周期life-cycle生态系统ecosystem生态学ecosystem生育率fertility实用主义pragmatism实在论realism实证主义positivism识别问题的要领problematic时间地理学time-geography时空会聚time-space convergence时空趋同time-space convergence时空压缩time-space compression时空延展time-space distanciation时空预测模型space-time forecasting model 市场market市场交换market exchange市场潜能模型market potential model市场区分析market area analysis市场指向market orientation市民身份citizenship世界城市world city世界系统分析world-system analysis适度人口optimum population收益revenue收益面revenue surface守护者gatekeepers数据分析data analysis数据库database数量革命quantitative revolution数字化digitizing水利社会hydraulic society私人和公共领域private and public spheres私有化privatization死亡率mortality搜索行为search behaviour酸雨acid rain随机过程stochastic processT泰勒主义Taylorism弹性积累flexible accumulation探索数据分析exploratory data analysis 探险exploration特大城市区megalopolis特殊性idiographic体育地理学sport , geography of天然地区nature调整restructuring通达性accessibility通勤commuting通则性nomothetic同化assimilation同批人cohort统计地图cartogram统计学statistics投入—产出input-output投影projection投资investment投资层次layers of investment突变论catastrophe theory图解法iconography图论graph theory图形能力graphicacy土地改革land reform土地利用调查land use survey土地占有land tenure推动主义boosterism推理inference退出、抱怨和信任exit , voice and loyalty脱离secessionW外部经济external economies外部性externalities外飞地enclave外界externalities外延式研究extensive research网络network微观模拟microsimulation危机crisis韦伯模型Weber model围地enclosure唯心主义idealism维护preservation维也纳学派；维也纳小组Vienna Circle (Wiener Kreis)温室效应greenhouse effect文本text文化culture文化霸权hegemony , effect文化地理学cultural geography文化景观culture landscape文化区culture area文化生态学culture ecology文化政治学culture politics文化资本culture capital文化核心culture heart文明社会civil society稳定人口stable population问卷questionnaire问题的构成problematic污染pollution无差异曲线indifference curve无地方性placelessness无家可归homelessness无序资本主义disorganize capitalism无政府主义anarchismX习性habitus系统system系统分析system analysis下层阶级underclass下等街区skid row显示偏好分析revealed preference analysis 显著性检验significance text现代化modernization现代性modernity现代主义modernism现象环境phenomenal environment现象学phenomenology线性规划linear programming相关correlation相互依赖interdepedence相互作用interaction乡村rural乡村地理学rural geography乡村规划rural planning乡村社区rural community想当然的世界taken-for-granted world消费地理学consumption, geography of消费者服务业consumer service小农peasent效率effciency效用utility效用理论utility theory心脏地带heartland新城new town新古典经济学neoclassical economics新国际劳动分工new international division of labour (NIDL)新李嘉图经济学neo-Ricardian economics新殖民主义neocolonialism信息城市information city信息论information theory形态morphology形态测量morphometry形态发生morphogenesis形态学morphology行为空间action space行为behaviour行为地理学behavioural geography行为环境behavioural environment性别与地理学gender and geography性与地理学sexuality and geography性质nature休闲recreation休闲地理学leisure, geography of修辞学rhetoric需求曲线demand curve畜牧pastozalism选举地理学electoral geography学会societies雪带snowbelt循环recyclingY亚细亚生产方式Asiatic model of production 演替succession验证数据分析confirmatory data analysis 阳光带/雪带sunbelt/snowbelt样方quadrat(e)遥感remote sensing野外性wilderness野外工作fieldwork依附dependence依附带zone of dependence一般线性模型general linear model一般系统论general systems theory一体化integration医学地理学medical geography遗产制度inheritance system移动mobility移居diaspora移民劳动力migrant labour意境地图mental map意识形态ideology异化alienation因子分析factor analysis因子复合体compage因子生态学factorical ecology应用地理学applied geography游牧transhumance游牧生活nomadism有序资本主义organize capitalism友邻效应friend-and-neibours effect语言language语言与方言地理学language and dialect, geography 语义（学）差别semantic difference预测forecasting预测寿命life expectancy预算estimate预言predicition原料指向material orientation援助aid运费率freight rate运输成本transport cost运输方式划分modal spilt运输问题transportation problemZ再分配redistribution暂时城市化temporary urbanization增长growth增长的极限limits to growth增长极growth pole增长阶段stages of growth占据occupancy整体论holism正态分布normal distribution证伪falsification政治地理学political geography政治经济学political economy芝加哥学派Chicago School殖民主义colonialism指令经济command economy治安地理学policing , geography of滞后hysteresis中间机会intervening opportunities中心地理论central place theory中心化centralization中心商务区central business district (CBD)中心图学centrography中央计划central planning种植园plantation种族race种族隔离apartheid种族中心主义ethnocentrism种族主义racism重力模型gravity model重商主义模式mercantile model主成分分析principal components analysis (PCA)主导产品staple主导产品理论staples theory主权sovereignty住房阶层housing class住房研究housing studies专家系统expert systems专题地图thematic map追溯法retrospective approach资本capital资本循环circuit of capital资本主义capitalism资源resource资源管理研究resource management资源管理部门resource management资源评价resource evaluation自发聚落spontaneous settlement自给农业subsistence agriculture自决权self-determination自然nature自然区natural area自然主义naturalism自然资源natural resource自相关autocorrelation自由布局型工业footloose industry自由港free port自由贸易区free trade area宗教地理学religion , geography of纵向课题vertical theme纵向数据分析longitudinal analysis租金rent最佳城市规模optimum city size最近相邻分析nearest neighbour analysis 最小最大化准则maximum criterion最优化模型optimization model。

Analysis on Model for Teaching Critical Thinking

Analysis on Model for Teaching Critical Thinking

- 117 -校园英语 / 基础教育研究Analysis on Model for Teaching Critical ThinkingBeijing Foreign Language School Affiliated to BFSU/Miao ZhouIntroductionThe basic aim of social studies is to help students make informed decisions when they face public and political issues. And how well the critical thinking skills one person acquires results in his effective participation in his future public life. Therefore, it is generally agreed that it is necessary to develop students ’ critical thinking skills.5-Step Model for Teaching Critical Thinking Step 1 Setting Learning GoalsSetting learning goals is an effective way to make the students to do what is allowed when they are in the class. And in order to realize critical thinking, the learning goals should be in accordance with the higher levels of Bloom ’s taxonomy, which consists of five levels of knowledge, comprehension, application, analysis, synthesis and evaluation from the bottom to the top. In the knowledge level, the students are expected to answer the simple questions like what, who and acquire the ability to describe, state and list what they have learned. Moving to the comprehension level, the students are expected to summarize, explain, paraphrase, compare and contrast what they have learned in the class. And in the application level, the students are expected to apply, construct, solve, discover and show what they have learned. Additionally, the students are expected to examine, classify, categorize, differentiate and analyze in the analysis level. While in the synthesis level, the students are expected to combine, construct, create, role-play and suppose. And in the highest evaluation level, the students are expected to assess, criticize, recommend, predict and evaluate. Therefore, scientific learning goals would encourage the desired behavior and realize the expected behavioral response. Thus it is important to set learning goals according to Bloom ’s taxonomy to accelerate the students ’ movement toward the critical thinking.Step 2 Teach through QuestioningQuestioning stands as a crucial part of teaching process. It makes it possible for the teachers to get a fully comprehensive of how well their students have learned. And it serves as the basis for further extension of students ’ ideas and understandings. In addition, questions are vital tools to establish interaction between teachers and students and they are a way to challenge the students of defend their positions to realize critical thinking. When teachers prepare their classes, they should consider the purpose of each question and try to select an appropriate level and the kind of questions to accomplish the purpose.Step 3 Practice before AssessNowadays, active learning has been adopted all over the world and it has been proved to be a much more effective way for students to learn more in a more enjoyable environment. While selecting learning activities, the teachers should bear in mind two principles ：first, activities should be selected from three components ：Information and Ideas, Experience, and Reflective Dialog. Information and Ideas consist of the primary and secondary sources the students could access in class, outside class or online. Experience consists of doing, observing and simulations. And Reflective Dialog consists of papers, portfolios and journaling. Second, direct types of learning activities, like in-class dialog, are preferred. Additionally, teachers should encourage students to perform an in-depth reflective dialog to find out what they have learned and what else they should learn, during which the students could gradually acquire the critical thinking.Step 4 Review, Refine and ImproveBy continually refine their courses, the teachers could make sure that their instructional techniques are helpful to develop the critical thinking skills of their students. To realize it, the teachers should monitor closely the learning activities and review what have happened during the process. In addition, the feedback of the students is also a guideline for improving the course.Step 5 Feedback and AssessmentAssessment and other kind of feedback are an effective way to evaluate the quality of students ’ learning. The teachers should make sure that all students fully understand the criteria of their assessment and provide good feedback to their students as often as possible to help them reflective their behaviors so that they could develop critical thinking during the process.ConclusionIn conclusion, critical thinking is helpful for students to control their own learning and make informed decisions when they face public and political issues. This paper introduces a 5-step teaching model to develop critical thinking for reference.References:[1]Yajie,Wu,Li,Chen and Hong,Zhao.Analysis on Teaching Model to Develop Critical Thinking.E-education Research,2014(11):71- 77.[2]Zhenhua,Chen.Deputy and Inspiration of Critical Thinking Development.Journal of Higher Education,2014(9):56-63.Copyright©博看网 . All Rights Reserved.。

高三英语论文观点论证分析练习题40题含答案解析

高三英语论文观点论证分析练习题40题含答案解析

高三英语论文观点论证分析练习题40题含答案解析1. The increase in air pollution is mainly due to _____.A. more cars on the roadB. less trees in the cityC. fewer factories producing wasteD. more people using public transportation答案解析：A。

选项A，路上汽车增多会排放更多尾气导致空气污染加剧，符合题意。

选项B，城市中树少不是空气污染增加的主要原因。

选项C，更少的工厂产生废物会减少污染而不是增加污染。

选项D，更多人使用公共交通会减少污染而不是增加污染。

本题考查因果论证，根据常识可知汽车尾气是空气污染的主要原因之一。

2. The development of technology has led to _____.A. less communication between peopleB. more face-to-face conversationsC. increased isolation of individualsD. better understanding among people答案解析：C。

选项A，科技发展不一定会导致人们交流减少。

选项B，科技发展不一定会带来更多面对面交谈。

选项C，科技发展使得人们更多地依赖电子设备，可能会增加个人的孤立感。

选项D，科技发展不一定会让人们之间更好地理解。

本题考查因果论证，科技发展可能带来一些负面效应，如个人孤立。

3. The popularity of online shopping is because of _____.A. higher prices of productsB. inconvenience of going to storesC. less variety of goodsD. longer delivery time答案解析：B。